Subsequently, we evaluate our approach with different sizes of fully-labeled set \({{\mathcal {D}}^{c}}\) . Similar to [1]}, we use 200, 400, 800 and 1464 images with strong annotations to train the clean model \(\mathcal {C}\)  respectively, and compare to the different baselines which are trained only on \({{\mathcal {D}}^{c}}\) . As shown in Table REF , our method achieves an improvement of 5.0% to 16.9% (v1-vgg16) and 2.5% to 11.2% (v2-resnet101) over the baselines for different sizes splits. More surprisingly, our performance on val set outperforms the fully-supervised model (76.5%) by 0.6% using v2-resnet101 segmentation network. Notably, the approach works well even with only 1.89% (\({{\mathcal {D}}^{c}}\)  = 200) of fully-labeled images, which illustrates that our method can provide a good training signal for the weak set so as to largely alleviate the problem of lacking of segmentation labels. The quantitative results on per-class IoU are shown in Table REF  and REF , and the qualitative results are shown in Figure REF .
